Owing to the rapid development of multimedia technology in recent years, human face recognition technologies have been widely used in various application fields. Facial recognition is useful and important in occasions where accurate identification of persons is the only way to protect one's own, organizations' or countries' interest, e. g. at immigration check points, office security, bank account management, counter-terrorism, etc.
Facial recognition has advantages over other biometric human recognition traits, e. g. finger prints, handprints, voice recognition, retinal recognition, and signatures as it is less invasive, encompasses more details for the recognition process to work on and requires less cooperation of those under investigation.
Human minds reflexively recognize familiar faces. Hardware of automatic facial recognition to date is available due to rapid development in the area of microcomputers. The software of facial recognition is largely based on facial topological morphing, as shown in FIG. 1.
In a biometric system, facial recognition may have 2 aspects. First, facial authentication is defined as a one-to-one match between the face under investigation against a known facial image of a database. Second, facial identification is defined as a one-to-many facial matching between the face under investigation versus multiple faces of a database.
Such matching is largely carried out by comparison of facial morphologies in face authentication and identification processes, typically using facial topological morphing methods. Common sources of errors in facial recognition are ambient illumination, facial position, facial expression, aging, hair style, face wears/pierces, etc.
These errors may be minimised by putting the face under investigation in a standardised positioning under standardised illumination. However, factors like aging, hair style, facial wear and piercings may still be difficult to control. Subtle differences between individuals e. g. between twins, may give rise to further error sources in facial recognition when using topology morphing method.
Moreover, errors related to facial positioning and ambient illumination of the face under investigation may be impossible to control in non-standardised positions, e. g. group photos, in motion video or surveillance camera.
Such errors imply much information load when performing the comparison calculation, consume too much time and resources and compromise accuracy of facial recognition.
This gives rise to a need for alternative method(s) in facial recognition processes without such errors. Linear and angular measurements between anthropological landmarks may overcome such errors. However, there is not yet any idea which lines or angles of the face are to be measured. Which lines or angles of the face are similar within the same ethnic group? Which lines or angles of the face under investigation are unique? The knowledge of such unique measurements between facial anthropological landmarks in the same ethnic group is of utmost importance for such an approach of facial recognition.
Big data is needed to enable such a method of facial recognition. In turn, measuring lines and angles between facial anthropological landmarks and assessing the reproducibility of such measurements in specific ethnic groups will generate big data.
Therefore, in order to overcome the drawbacks exist in the prior art, a method for face recognition and analysis is provided. The particular design in the present invention does not only solve the problems described above, but it is also easy to be implemented. Thus, the present invention has potential applications in the industry.